Update app.py

app.py

@@ -23,7 +23,7 @@ def download_dataset():
     print("📥 Downloading dataset from Hugging Face...")
 
     try:
-        response = requests.get(DATASET_URL, timeout=15)
+        response = requests.get(DATASET_URL, timeout=10)
 
         if response.status_code == 200:
             with open(DATASET_PATH, "wb") as file:
@@ -81,7 +81,7 @@ class PredictionInput(BaseModel):
     case_problem: str
 
 @app.post("/predict/")
-def predict_feedback(data: PredictionInput):
+async def predict_feedback(data: PredictionInput):
     """ Predicts feedback based on Case Problem """
     if model is None:
         return {"error": "Model is not trained yet."}
@@ -92,13 +92,15 @@ def predict_feedback(data: PredictionInput):
     if case_problem_lower not in df["Case Problem"].values:
         return {"error": "Invalid case problem. Please enter a valid category from the dataset."}
 
-    case_problem_encoded = encoder.transform([case_problem_lower])
-    prediction = model.predict([[case_problem_encoded[0]]])
-    feedback_predicted = encoder.inverse_transform(prediction)[0]
-
-    return {"Predicted Feedback": feedback_predicted}
-
-# ✅ Gradio UI
+    try:
+        case_problem_encoded = encoder.transform([case_problem_lower])
+        prediction = model.predict([[case_problem_encoded[0]]])
+        feedback_predicted = encoder.inverse_transform(prediction)[0]
+        return {"Predicted Feedback": feedback_predicted}
+    except Exception as e:
+        return {"error": str(e)}
+
+# ✅ Gradio UI with async execution
 def gradio_interface(case_problem):
     if model is None:
         return "Model not trained yet."
@@ -109,11 +111,13 @@ def gradio_interface(case_problem):
     if case_problem_lower not in df["Case Problem"].values:
         return "Invalid case problem. Please enter a valid category from the dataset."
 
-    case_problem_encoded = encoder.transform([case_problem_lower])
-    prediction = model.predict([[case_problem_encoded[0]]])
-    feedback_predicted = encoder.inverse_transform(prediction)[0]
-
-    return f"Predicted Feedback: {feedback_predicted}"
+    try:
+        case_problem_encoded = encoder.transform([case_problem_lower])
+        prediction = model.predict([[case_problem_encoded[0]]])
+        feedback_predicted = encoder.inverse_transform(prediction)[0]
+        return f"Predicted Feedback: {feedback_predicted}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # ✅ Start both API & Gradio
 def start_app():
@@ -124,7 +128,7 @@ def start_app():
         outputs="text", 
         live=True  # ✅ Ensures Gradio UI updates properly
     )
-    gr_interface.launch(share=True)  # ✅ Makes app accessible externally
+    gr_interface.launch(share=True, debug=True)  # ✅ Debugging enabled to see errors
     uvicorn.run(app, host="0.0.0.0", port=8000)
 
 if __name__ == "__main__":